Machine learning tools for pattern recognition in polar climate science

Over the past four decades, the inexorable growth in technology and subsequently the availability of Earth-observation and model data has been unprecedented. Hidden within these data are the fingerprints of the physical processes that govern climate variability over a wide range of spatial and temporal scales, and it is the task of the climate scientist to separate these patterns from noise. Given the wealth of data now at our disposal, machine learning methods are becoming the tools of choice in climate science for a variety of applications ranging from data assimilation, to sea ice feature detection from space. This talk summarises recent developments in the application of machine learning methods to the study of polar climate, with particular focus on Arctic sea ice. Supervised learning techniques including Gaussian process regression, and unsupervised learning techniques including cluster analysis and complex networks, are applied to various problems facing the polar climate community at present, where each application can be considered an individual component of the larger sea ice prediction problem. These applications include: seasonal sea ice forecasting, improving spatio-temporal data coverage in the presence of sparse satellite observations, and illuminating the spatio-temporal connectivity between climatological processes.